code: def execute_job(
      idx: int,
      overrides: Sequence[str],
      hydra_context: HydraContext,
      config: DictConfig,
      singleton_state: Dict[Any, Any],
      gpu_idx: int,
  ):
      """
      Creates a process that launches a "single" job that is identical in config + updated with sweep hyperparams.
      Since a different process is being used, CUDA can work in non-ddp mode without issue.
      Attempting ddp when using this script will not work as ddp cannot be used in shared contexts.
  
      Args:
          idx: Global index of the job.
          overrides: List of str overrides that correspond to this job
          hydra_context: Hydra Context used to load the sweep params into the global config
          config: Global config that will be updated with sweep hyper parameters.
          singleton_state: Hydra state.
          gpu_idx: The GPU ID on which this process will be run.
  
      Returns:
          - The Process object that corresponds to this sweep
          - The JobReturn object holding some metadata about this run
      """
      # Required by Hydra (lookup other Hydra Launchers for details)
      setup_globals()
      Singleton.set_state(singleton_state)
  
      # Update base config with overrides to create sweep config
      sweep_config = hydra_context.config_loader.load_sweep_config(
          config, list(overrides)
      )
      with open_dict(sweep_config):
          sweep_config.hydra.job.id = "{}_{}".format(
              sweep_config.hydra.job.name, idx
          )
          sweep_config.hydra.job.num = idx
      HydraConfig.instance().set_config(sweep_config)
  
      # Setup a directory where the config will temporarily be stored.
      script_path = os.path.join(os.getcwd(), sys.argv[0])
      script_path = os.path.abspath(script_path)
  
      hash_salt = "|".join(
          [script_path, str(OmegaConf.to_yaml(config))]
      ).encode("utf-8")
      hash_val = hashlib.sha256(hash_salt).hexdigest()
  
      config_dir = os.path.join(os.getcwd(), "hydra_cfg", str(hash_val))
      if not os.path.exists(config_dir):
          os.makedirs(config_dir, exist_ok=True)
  
      task_cfg = copy.deepcopy(sweep_config)
  
      # Remove hydra from sweep config
      # This is done to prevent recursive call to multirun launcher in Hydra.
      with open_dict(task_cfg):
          task_cfg.pop("hydra", "")
  
      # Save the current jobs config to directory
      temp_config_name = f"config_{idx}.yaml"
      temp_config = os.path.join(config_dir, temp_config_name)
      OmegaConf.save(task_cfg, temp_config)
  
      # Compute the overides as a dict
      overrides = OmegaConf.to_container(config.hydra.overrides.task)
  
      # Check and replace trainer.devices in config with gpu_idx
      found_devices = False
      gpu_override = f"trainer.devices=[{gpu_idx}]"
      for oidx, val in enumerate(overrides):
          if "trainer.devices" in val:
              overrides[oidx] = gpu_override
              found_devices = True
  
      if not found_devices:
          overrides.append(gpu_override)
  
      # Build launch command
      # Note: We depend on PTL doing the right thing since this command has global visibility of all CUDA_VISIBLE_DEVICES
      cmd = [
          "python",
          script_path,
          "--config-path",
          config_dir,
          "--config-name",
          temp_config_name,
          *overrides,
      ]
  
      # Launch the subprocess; pipe the stderr
      # NOTE: If this hangs due to some reason after prolonged training, it means that the stderr pipe buffer
      # has become full at the OS level and we need to explicitly empty it (either parallel thread or manually
      # call proc.communicate(). It should not happen in general case as stderr is filled only in case retcode != 0
      # If it does happen though, implement the code here
      # https://stackoverflow.com/questions/39607172/python-subprocess-popen-poll-seems-to-hang-but-communicate-works
      proc = subprocess.Popen(cmd, stderr=subprocess.PIPE)
  
      # Setup data thread for stderr
      std_error_buffer = []
      # Trivial thread just reads lines from stdout into the list
      drainerthread = threading.Thread(
          target=std_error_buffer.extend, args=(proc.stderr,)
      )
      drainerthread.daemon = True
      drainerthread.start()
  
      # Construct JobReturn object for Hydra
      res = JobReturn()
      res.cfg = task_cfg
      res.overrides = overrides
      res.hydra_cfg = config
      res.working_dir = os.getcwd()
      res.return_value = None
  
      return proc, res, (std_error_buffer, drainerthread)
  
imports: import copy
  import hashlib
  import os
  import subprocess
  import sys
  import threading
  import time
  from dataclasses import dataclass
  from pathlib import Path
  from typing import Any, Dict, Optional, Sequence
  import torch
  from hydra.core.config_store import ConfigStore
  from hydra.core.hydra_config import HydraConfig
  from hydra.core.plugins import Plugins
  from hydra.core.singleton import Singleton
  from hydra.core.utils import JobReturn, JobStatus, configure_log, filter_overrides, setup_globals
  from hydra.plugins.launcher import Launcher
  from hydra.types import HydraContext, TaskFunction
  from omegaconf import DictConfig, OmegaConf, open_dict
  from nemo.utils import logging
label_desc: Use image node_id 2 for calling a global function with example usage: execute_job(idx, overrides, hydra_context, config, singleton_state, gpu_idx) and returns: proc,  res,  std_error_buffer,  drainerthread